Study On Mechanical Vibration Analysis And Diagnosis Methods Based On Emperical Mode Decomposition

Based on the "Research on method for mechanical vibration source semi-blind source separation and reconstruction" (National Nature Science Fund Project, No: 50675194), and the "Research on the condition monitoring and fault diagnosis techniques and its application system of supercritical and ultra-supercritical steam turbines and pressure generating units" (High Technology Research and Development Program of China, No: 2008AA04Z410), aiming at nonstable charateristics of vibration signals of mechanical system, introduced empirical mode decomposition (EMD) method into vibration analysis and fault diagnosis for mechanical system, research on phsical application basis, end effect and false modes of EMD, presented solution scheme for mechanical vibration mode effective analysis and fault feature extraction method, set up EMD—2D-HMM diagnosis model, and carried out a large number of simulation and experiment. The details were as follows:In Chapter one: The method of the situation monitoring and fault diagnosis of the rotating machine which uses vibration signals and the techniques were discussed. The problems existed in the techniques of vibration information operation were analyzed. Feasibility and importance of introducing EMD into fault diagnosis of rotating machines were disputed. Through the study and analysis applicaition of EMD in the different fields, concluded the next research development of EMD method. Finally, according to signal detection, pretreatment, feature extraction and mode classification related to meachanical fault diagnosis, described the background to carry out this research and its details, the scheme and the innovation points of this dissertation.In Chapter two: Researched the phsical meaning of vibration signal analysis with EMD on view of mechanical vibration characteristics, presented an analysis method of vibrating mode of mechanical system based on EMD. The fundamental principle and algorithm procedure of EMD method were discussed in detail. The vibration model of mechanical system was established, studied the vibrating characteristics and its EMD analysis of single-freedom-degree and multi-degree-of-freedom system, the relationship discussion between mechanical vibrating mode and intrinsic mode function (IMF) was introduced. Using transverse vibration response of beam supported of both ends as an example. An important phenomenon that the existence of corresponding relation between system vibrating mode and IMF which obtained by EMD for vibating signal with experimental results, which established substantial theoretical foundation for EMD method in the application of condition monitoring and fault diagnosis for rotating machines, solved phsical difficult problem of EMD for vibration mode analysis for mechanical sysytem.In Chapter three: The origin of EMD method's end effect and its harmful influence in vibration signal analysis, the front research at home and abroad about restraining end effect methods were summarized. Aiming at vibrating characteristic of rotating machine, drawbacks of the available signal extension methods for restraining end effect were pointed out. A new method of restraining end effect based on end optimization symmetric extension (EOSE) was presented. Firstly, assumed two ends of original signal as unknown value, and extended in a point-symmetrical manner with end-point as its center. Secondly, constituted deviation error evaluation function concerning original signal, and minimized the function, so that obtained the two optimization end-points value. Extended in a point symmetrical manner with new end-point value, the obtained up and down envelops maximally approached original signal end-point, which restrained the envelops divergence in EMD algorithm. Finally, in the process of filtering intrinsic mode function (IMF) discard the extended data, which release end effect to original signal outside and maximally reduced distortion of original signal. Simulation and experiment show that the proposed method could restrain end effect effectively and precisely extract classic fault feature of vibration signal of rotating machine.In Chapter four: Aiming at the defect of bring high-frequency nosie mode and low-frequency false mode by EMD for mechanical vibration signals, stastistic charactistics of white nosie with EMD method were researched. A method based on the characteristics of white noise is presented to test the validity of mechanical vibrating mode. This method is a developed algorithm of empirical mode decomposition (EMD), which adaptively eliminated high frequency noise components and low frequency false components by applying the characteristics of normalized white noise under EMD, so the intrinsic mode set reflecting actual physical process of vibration signal are obtained. In the whole feature process, the construction of general basis function described by some parameters and related filter function is unnecessary, and any prior information about the observed signal is no more required, which felicitates the method for a wide variety of applications. Both computer simulation and rotor set experimental results verify this approach is practicable and effective, improved the precision of feature extraction.In Chapter five: Starting from de-noising request of nonstationary vibration signals of rotating machine, respectively described four present common filter methods including digital filter, Kalman filter, wavelet de-noise and EMD de-noise, depicted de-noising principle and suit situation of the above-mentioned de-noising methods, explicated the defects of four filter methods for de-noising of nonstationary vibration signals of rotating machine, focusing on the research of mode mixing brought by EMD de-noising method. Appling dyadic scales decomposition characteristics of normal distribution white noise with EMD, an ensembled algorithm of EMD de-noising method was presented. Simulation numerical signal and experimental signal of rotor running state are used to test and compare the performances of the method and EMD based de-noising method and wavelet de-noising method. The results show that the ensembling EMD based noise cancellation method presented in the paper has more effective de-noising performance, not only eliminates random noise, but suppresses intensity noise and extracts vibration intrinsic modes that reflect real physical meaning of signal.In Chapter six: Physical relationship between instantaneous energy and structural state variation of system was studied therotically, instantaneous energy of mechanical vibration signal changed with variable structural state in the machine running state, different instantaneous energy distribution stand for different fault. By EMD and Hilbert Transformation (HT) methods, the concept of vibration mode instantaneous energy was brought up, a new instantaneous energy distribution characteristics extraction method based on EMD and HT. In terms of depicting signals in time-frequency domain, instantaneous energy distribution and Hilbert margin spectra feature of mechanical vibration signals were extracted. The results of rotor experimental set proved this method is effective, could identify different fault type.In Chapter seven: The advantages of 2D-HMM method in the aspect of time sequence mode classification for nonstationary vibration signals of rotating machine was discussed, elaborated the essential concept and diagnosis principle of 2D-HMM method. The integration necessity and feasibility of EMD and 2D-HMM for fault diagnosis was analyzed. A diagnosis model based on EMD-2D-HMM was set up. Firstly, decomposed mechanical vibration signal into a number of intrinsic mode function components applying EMD, extracted instantaneous energy distribution and frequency energy spectrum of effective vibration mode, which could be used to set up time-domain and frequency-domain observation sequence; secondly, set up 2D-HMM classifying model by the extracted observation sequence, so constitute 2D-HMM fault diagnosis model database, and judged running state of machine Through calculating the maximal log-likelihood. An experiment system was set up on the basis of rotor experimental table. Experimental results show that EMD-2D-HMM method had higher classification performance and diagnosis availability.In Chapter eight: Based on the above research of EMD-2D-HMM fault diagnosis method, the concrete demand on designing and developing corresponding fault diagnosis system were analyzed. The overall framework of fault diagnosis system based on EMD-2D-HMM was devised, and the composition and design scheme of data acquisition system and preprocessing unit and fault diagnosis software package were studied in detail. The fault diagnosis software system based on EMD-2D-HMM was designed and developed. System feasibility was tested with the measured data of experimental set.In Chapter nine: The concolutions are presented and some advanced topics in the proposed methodology that needs further investigation in future are presented and addressed.